Deep deterministic policy gradient algorithm: A systematic review

Sumiea, Ebrahim Hamid; Abdulkadir, Said Jadid; Alhussian, Hitham; Al-Selwi, Safwan Mahmood; Alqushaibi, Alawi; Ragab, Mohammed Gamal; Fati, Suliman Mohamed

doi:10.1016/j.heliyon.2024.e30697

reviewHeliyonMay 1, 2024GOLD OA

Deep deterministic policy gradient algorithm: A systematic review

EHEbrahim Hamid Sumiea SJSaid Jadid Abdulkadir HAHitham Alhussian SMSafwan Mahmood Al-Selwi AAAlawi Alqushaibi

Universiti Teknologi Petronas · Prince Sultan University

PubMed

Indexed incrossrefdoajpubmed

Abstract

Deep Reinforcement Learning (DRL) has gained significant adoption in diverse fields and applications, mainly due to its proficiency in resolving complicated decision-making problems in spaces with high-dimensional states and actions. Deep Deterministic Policy Gradient (DDPG) is a well-known DRL algorithm that adopts an actor-critic approach, synthesizing the advantages of value-based and policy-based reinforcement learning methods. The aim of this study is to provide a thorough examination of the latest developments, patterns, obstacles, and potential opportunities related to DDPG. A systematic search was conducted using relevant academic databases (Scopus, Web of Science, and ScienceDirect) to identify 85…

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160

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FWCI: 49.83
Percentile: 100%
References: 146

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7

Topics & keywords

Topics

Keywords

Reinforcement learning
Artificial intelligence
Computer science
Strengths and weaknesses
Field (mathematics)
Resource (disambiguation)
Key (lock)
Management science

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